dc.creator | Lamata Manuel, Lucas | es |
dc.date.accessioned | 2021-05-03T10:29:46Z | |
dc.date.available | 2021-05-03T10:29:46Z | |
dc.date.issued | 2020-07 | |
dc.identifier.citation | Lamata Manuel, L. (2020). Quantum machine learning and quantum biomimetics: A perspective. Machine Learning: Science and Technology, 1 (3), 033002. | |
dc.identifier.issn | 2632-2153 | es |
dc.identifier.uri | https://hdl.handle.net/11441/108346 | |
dc.description.abstract | Quantum machine learning has emerged as an exciting and promising paradigm inside quantum
technologies. It may permit, on the one hand, to carry out more efficient machine learning
calculations by means of quantum devices, while, on the other hand, to employ machine learning
techniques to better control quantum systems. Inside quantum machine learning, quantum
reinforcement learning aims at developing ‘intelligent’ quantum agents that may interact with the
outer world and adapt to it, with the strategy of achieving some final goal. Another paradigm
inside quantum machine learning is that of quantum autoencoders, which may allow one for
employing fewer resources in a quantum device via a training process. Moreover, the field of
quantum biomimetics aims at establishing analogies between biological and quantum systems, to
look for previously inadvertent connections that may enable useful applications. Two recent
examples are the concepts of quantum artificial life, as well as of quantum memristors. In this
Perspective, we give an overview of these topics, describing the related research carried out by the
scientific community | es |
dc.description.sponsorship | Ministerio de Ciencia, Innovación y Universidades de España-PGC2018-095113-B-I00 | es |
dc.description.sponsorship | Ministerio de Ciencia e Innovación de España-PID2019-104002GB-C21 | es |
dc.description.sponsorship | Ministerio de Ciencia, Innovación y Universidades de España (MCIU), Agencia Española de Investigación (AEI) y Fondo Europeo de Desarrollo Regional (FEDER)-PID2019-104002GB-C22 | es |
dc.format | application/pdf | es |
dc.format.extent | 12 p. | es |
dc.language.iso | eng | es |
dc.publisher | 2632-2153 | es |
dc.relation.ispartof | Machine Learning: Science and Technology, 1 (3), 033002. | |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.subject | quantum machine learning | es |
dc.subject | quantum biomimetics | es |
dc.subject | quantum artificial intelligence | es |
dc.subject | quantum reinforcement learning | es |
dc.subject | quantum autoencoders | es |
dc.subject | quantum artificial life | es |
dc.subject | quantum memristors | es |
dc.title | Quantum machine learning and quantum biomimetics: A perspective | es |
dc.type | info:eu-repo/semantics/article | es |
dcterms.identifier | https://ror.org/03yxnpp24 | |
dc.type.version | info:eu-repo/semantics/publishedVersion | es |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | es |
dc.contributor.affiliation | Universidad de Sevilla. Departamento de Física Atómica, Molecular y Nuclear | es |
dc.relation.projectID | PGC2018-095113-B-I00 | es |
dc.relation.projectID | PID2019-104002GB-C21 | es |
dc.relation.projectID | PID2019-104002GB-C22 | es |
dc.relation.publisherversion | https://iopscience.iop.org/article/10.1088/2632-2153/ab9803 | es |
dc.identifier.doi | 10.1088/2632-2153/ab9803 | es |
dc.journaltitle | Machine Learning: Science and Technology | es |
dc.publication.volumen | 1 | es |
dc.publication.issue | 3 | es |
dc.publication.initialPage | 033002 | es |
dc.contributor.funder | Ministerio de Ciencia, Innovación y Universidades (MICINN). España | es |
dc.contributor.funder | Ministerio de Ciencia e Innovación (MICIN). España | es |
dc.contributor.funder | Ministerio de Ciencia, Innovación y Universidades (MICINN). España | es |
dc.contributor.funder | Agencia Española de Investigación (AEI) | es |
dc.contributor.funder | European Commission (EC). Fondo Europeo de Desarrollo Regional (FEDER) | es |